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Keywords = differential constellation trace figure

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19 pages, 3471 KiB  
Article
Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
by Hua Fu, Hao Dong, Jian Yin and Linning Peng
Entropy 2024, 26(1), 38; https://doi.org/10.3390/e26010038 - 29 Dec 2023
Cited by 3 | Viewed by 4292
Abstract
The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of [...] Read more.
The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform. Full article
(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)
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